code: def load_model(
      bento_model: str | Tag | bentoml.Model,
  ) -> mlflow.pyfunc.PyFuncModel:
      """
      Load the MLflow `PyFunc <https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.PyFuncModel>`_ model with the given tag from the local BentoML model store.
  
      Args:
          bento_model: Either the tag of the model to get from the store, or a BentoML
              ``~bentoml.Model`` instance to load the model from.
  
      Returns:
          The MLflow model loaded as PyFuncModel from the BentoML model store.
  
      Example:
  
      .. code-block:: python
  
          import bentoml
          pyfunc_model = bentoml.mlflow.load_model('my_model:latest')
          pyfunc_model.predict( input_df )
      """  # noqa
      if not isinstance(bento_model, bentoml.Model):
          bento_model = get(bento_model)
  
      if bento_model.info.module not in (MODULE_NAME, __name__):
          raise NotFound(
              f"Model {bento_model.tag} was saved with module {bento_model.info.module}, not loading with {MODULE_NAME}."
          )
  
      return mlflow.pyfunc.load_model(
          bento_model.path_of(MLFLOW_MODEL_FOLDER)
      )
  
file_path: BentoML/src/bentoml/_internal/frameworks/mlflow.py
imports: from __future__ import annotations
  import logging
  import os
  import shutil
  import tempfile
  import typing
  from typing import TYPE_CHECKING
  import bentoml
  from bentoml import Tag
  from bentoml.exceptions import BentoMLException
  from bentoml.exceptions import MissingDependencyException
  from bentoml.exceptions import NotFound
  from bentoml.models import ModelContext
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(bento_model) and returns: mlflow